code: def __init__(
      self, hidden_channels: int, out_channels: int, dropout: float
  ):
      super().__init__()
      self.dropout = torch.nn.Dropout(p=dropout)
  
      self.conv1 = SAGEConv((-1, -1), hidden_channels)
      self.conv2 = SAGEConv((-1, -1), hidden_channels)
      self.lin = Linear(-1, out_channels)
  
imports: import os.path
  from typing import Dict, List, Tuple
  import pytorch_lightning
  import torch
  import torch.nn.functional
  from pytorch_lightning import LightningModule, Trainer
  from pytorch_lightning.callbacks import ModelCheckpoint
  from torch import Tensor
  from torchmetrics import Accuracy
  import torch_geometric.transforms
  from torch_geometric.data import Batch
  from torch_geometric.data.lightning import LightningNodeData
  from torch_geometric.datasets import OGB_MAG
  from torch_geometric.nn import Linear, SAGEConv, to_hetero
  from torch_geometric.typing import EdgeType, NodeType
label_desc: Use image node_id 1 to create a new GNN object from inherited base classes: torch.nn with example: obj = GNN(hidden_channels, out_channels, dropout)